Autonomous vehicles and methods of zone driving

ABSTRACT

Autonomous vehicles are capable of executing missions that abide by on-street rules or regulations, while also being able to seamlessly transition to and from “zones,” including off-street zones, with their our set(s) of rules or regulations. An on-board memory stores roadgraph information. An on-board computer is operative to execute commanded driving missions using the roadgraph information, including missions with one or more zones, each zone being defined by a sub-roadgraph with its own set of zone-specific driving rules and parameters. A mission may be coordinated with one or more payload operations, including zone with “free drive paths” as in a warehouse facility with loading and unloading zones to pick up payloads and place them down, or zone staging or entry points to one or more points of payload acquisition or placement. The vehicle may be a warehousing vehicle such as a forklift.

REFERENCE TO RELATED APPLICATIONS

This Application is a continuation of U.S. patent application Ser. No. 16/598,350, filed Oct. 10, 2019, now U.S. Pat. No. 11,372,425, which is a continuation of U.S. patent application Ser. No. 15/806,844, filed Nov. 8, 2017, which claims the benefit of and priority to U.S. Provisional Patent Application Ser. No. 62/419,308, filed Nov. 8, 2016, the entire content of all Related Applications being incorporated herein by reference.

FIELD OF THE INVENTION

This invention relates generally to autonomous vehicles and, in particular, to vehicles able to seamlessly transition between on-street rules or regulations and off-street zones having different rules or regulations.

BACKGROUND OF THE INVENTION

A road map or, alternatively, a roadgraph, is a graph network of information including roads, lanes, intersections, and the connections between these features. A roadgraph also typically includes a set of prescribed driving rules (speed limits, lane or road width defaults, whether turn right on red is allowed or disallowed, driving on the left or right hand sides of the roads, etc.) The roadgraph may also include one or more zones defined as areas, typically defined as a bounded polygonal area, that embody alternative driving conventions within that zone that differ from the rules defined for the roadgraph within which the zone is defined. One example is an automated tactical, or outdoor, forklift truck where, from work area to work area, the truck operates driving missions over roadways and follows normal driving rules, but at points of load pick-up or put-down, may enter a zone where alternative driving rules can be applied (for instance the truck may drive from any point to any other point without regards to lanes or right/left hand passing rules as long as no person, other vehicle, or obstacles is placed into danger of collision).

Autonomous vehicles basically traverse roadgraphs by finding and taking paths through the roadgraph from the vehicle's current location to a designated endpoint. As the graph is traversed, vehicle drive logic generates waypoints typically expressed as GPS coordinates (latitudes and longitudes) or some other equivalent location coordinates (for instance in Universal Transverse Mercator coordinates express locations as meters from an origin point). Then the vehicle drives these points, with possible small path variations to avoid obstacles, to get to the designated endpoint (or sometimes this might be called a checkpoint).

Associated with the roadgraph there may be global driving rules and parameters. In the rules of the road for the DARPA Urban Challenge [DARPA 2007 Rules] derived from California driving rules of the road, these rules included:

-   -   Rules for passing cars stopped on the road     -   Rules for intersection precedence (stopping and yielding to         cross traffic)     -   Rules prohibiting passing within a specified distance from         intersections     -   Maximum Speed limits

Associated with waypoints (or checkpoints) in the roadgraph there might be specific driving rules or behaviors that are invoked as the point is approached or as it is passed. In the rules of the road for the DARPA 2007 Rules, these included:

-   -   Stopping at a stop point—also anticipating other driver         behaviors at stop points in other lanes connected in the graph         to this stop point (i.e. collectively defining an intersection         and its specific intersection behavior)     -   Changing speed based on speed limits defined on a particular         segment of road in the graph (i.e. limits higher or lower that         the default maximum speed limit for the roadgraph)     -   Changes in authority to deviate from the planned path for         obstacle avoidance dependent on specified lane widths for a         particular road segment within the roadgraph     -   Lane change rules for the road segment (i.e. expressed to human         drivers as yellow solid lines of white solid lines that are         there to prevent lane changes)

Other rules could include whether turn right on red is allowed, whether U-turns are allowed, lane closures, special occupancy lanes (i.e. multiple occupancy lanes and perhaps in the future, autonomous vehicle lanes).

FIG. 1 shows how the DARPA roadgraph is defined [DARPA 2007 Route Network File (RNDF) and Mission Data File (MDF) Formats]. Most of the graph links the roadgraph to a series of road segments. These road segments are named, potentially have a unique speed limit, and define each lane along the road segment. The lanes are defined as a series of waypoints in GPS coordinates (but alternatively in another location defining form like UTM). Associated with each of these points can be driving rule properties like those already described. Furthermore some of the waypoints can also be defined a checkpoints that can be specified as destination locations.

Other map representations use similar encoding approaches. Ex: Google Maps codes points as KML [KeyHole Markup Language] which uses polylines to encode paths made up of compressed waypoint coding latitude, longitude and altitude. Then various properties are associated with these segments like speed limit, place names, etc. Ex: TIGER, Topologically Integrated Geographic Encoding and Referencing, was developed by the US Census Bureau and encodes path data as lines and polygons made up of latitude and longitude waypoints. Other properties are included as data blocks referencing locations or areas—how one encodes this data in a software application is left to the developer.

Note that one of the features of the DARPA graph is the definition of a zone. The idea of the DARPA zone is an area where the automated vehicle is free to drive from any zone entry point to any checkpoint defined in the zone or to a designated exit point from the zone through any path (usually the shorted path) that is free of obstacles. The DARPA zone models driving off road over an area of rugged terrain, but it are also an ideal representation for:

Parking areas Intersections (based on intersection precedence the automated vehicle will enter the intersection expecting all other drivers to respect their own yielding protocol so that should not be any other vehicle or pedestrian in the intersection that would interfere with the autonomous vehicle, however, as we all know some people violate these precedence rules creating the possible situation in the intersection where vehicles have to maneuver around each other and pedestrians or stop so as not to collide with them—this is the basic zone driving behavior, take any path out of the zone that does not collide with another vehicle, pedestrian, or obstacle).

Construction zones (using barriers that are set-up as obstacles to guide traffic around work areas)

In U.S. Pat. No. 8,509,982, the entire content of which is incorporated herein by reference, the concept of a zone is defined as locations where diving is challenging such as merges, construction zones, or other obstacles where the automated driving system is not capable. This example is that in a zone, driving rules might require the autonomous vehicle to alert the driver that the vehicle is approaching the zone so that the driver can take control of steering, acceleration, etc. In another example they suggest that entering a zone might be associated with an automatic broadcast from the vehicle to others in the surrounding area signaling an unsafe condition.

SUMMARY OF THE INVENTION

The present invention relates to autonomous vehicles, with one distinction being that vehicles configured in accordance with the invention are capable of executing missions that abide by on-street rules or regulations, while also being able to seamlessly transition to and from “zones,” including off-street zones, with their our set(s) of rules or regulations.

A driverless vehicle according to the invention includes a frame, platform or chassis with a powertrain driving a set of wheels, and further including steering and braking systems. An on-board memory stores roadgraph information, including driving rules and parameters associated with a coordinate system. A localization system determine the location of the vehicle relative to the roadgraph coordinate system, and an obstacle detection system is used to avoid structures external to the vehicle along the driving path.

An on-board computer, interfaced to the powertrain, and steering and braking subsystems is operative to execute commanded driving missions using the roadgraph information, taking advantage of the localization and obstacle detection systems. In accordance with the invention, the vehicle is further operative to execute missions including one or more zones, each zone being defined by a sub-roadgraph with its own set of zone-specific driving rules and parameters.

A mission may be coordinated with one or more payload operations, including zone with “free drive paths” as in a warehouse facility. As examples, a mission may include loading and unloading zones to pick up payloads and place them down; or a mission may take the vehicle from zone staging or entry points to one or more points of payload acquisition or placement. One or more roadgraphs may be embedded within a zone, thereby inheriting the driving rules and parameters specific to that zone.

The vehicle localization system may include GPS for latitude and longitude localization and visual sensing of environmental structures external to the vehicle, and may additionally include barcode location markers for localization overwatch indoors. The vehicle may be a warehousing vehicle such as a forklift.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows how the DARPA roadgraph is defined [DARPA 2007 Route Network File (RNDF) and Mission Data File (MDF) Formats];

FIG. 2 illustrates an alternative example of using the zone to change driving rules;

FIG. 3 depicts how the driverless roadgraph approach is typical of other DARPA Urban Challenge derived driverless vehicle control systems;

FIG. 4 illustrates how simple drive missions are typically described as a set of destination or checkpoints that the automated vehicle has to visit in the prescribed order;

FIG. 5 shows the plan used by one loader to enter the yard (Zone 1 through Checkpoint 1), find a load on the input side (Checkpoint 6), acquire a pallet, and move this pallet to the output side (Checkpoint 4). Then a second loader enters the yard from a different zone entry point (Checkpoint 3), finds and acquires a pallet from the output side (at Checkpoint 5), and takes the load to the left hand end of mission point (Checkpoint 7);

and

FIG. 6 shows how each warehouse of a multi-warehouse facility may be a submap of a larger map that encodes the streets interconnecting the warehouses; and

FIG. 7 is a schematic block diagram of a vehicle to which this invention is applicable.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Our definition of zone semantic is logically identical to that used by DARPA in its roadgraph definition and seems similar to that described by Google also. However, while DARPA defines a zone as an open free driving area and Google defines it as a “dangerous” area that might require passing of drive control back to the human operator, we defined the zone as a new sub roadgraph possibly with its own set of zone specific driving rules and parameters.

This more general definition encompasses the earlier forms cited. However, the definition is motivated by autonomous vehicles like tactical forklifts that move loads over short roadway segment that require obedience to regular road driving rules, but also free drive within loading and unloading zones to pick up payloads and place them down. Within these open drive zones, obstacles have to be detected and navigated around, while driving paths generated to take the vehicle from zone staging or entry points to point of payload acquisition or placement. Furthermore, in-zone operations to place the vehicle in locations must be coordinated with other payload operations like moving sensors into proper line of sight with payloads (i.e. positioning of the truck, its sensors, and its appendages to line up properly with respect to payloads), payload pick-up and put-down manipulations by forks or other manipulation appendages, and management of load centers of mass.

Another example of using the zone to change driving rules is shown in FIG. 2. In England, vehicles operate with the left hand lane rule while in Holland they operate on the right hand rule. One way to use zones is to call England a Zone of type Left and the European continent a zone of type Right. Most of the basic driving rules will be similar for each zone encompassed area but will be coded for left or right handed driving preferences respectively. This, then, identifies another property of the generalized zone. Roadgraphs can be embedded within a zone, inheriting the zone's defined default driving rules and properties. This includes embedding of smaller area zones as part of a roadgraph within the larger zone.

As shown in FIG. 3, the driverless roadgraph approach is typical of other DARPA Urban Challenge derived driverless vehicle control systems. It is built on four elements. The first is the vehicle localization system that always knows where the vehicle is relative to the roadgraph coordinate system to some minimum positional error. In our systems, this is accomplished by using what we call position different sensors (wheel encoders that count wheel revolutions, inertial sensor that measure gyroscoping and translational accelerations of the vehicle, which when integrated provide an estimate of orientation and location change, and magnetic compass which measures magnetic north) to provide immediate location change measurements combined with overwatch sensors (GPS measurements, visual landmarks fixed to known locations on the ground that can be detected through video or laser radar sensors) bounding the location error derived by integrating the difference sensors over time.

Driverless vehicles often depend heavily upon GPS overwatch for location detection, even to the accuracy needed for road lane keeping. Automated loaders and carriers also often employ visual sensors (video or laser radar sensors) to find and localize to visual landmarks because these sensor are also necessary to find loads that are located at an approximate location (i.e., pallets might not be placed exactly at the designated location, but only approximately there and they then have to be found so as to be acquired for transport using visual sensors).

In our systems, we employ both GPS for latitude and longitude localization, and overwatch out-of-doors and visual sensing of features like doorways, walls, hallways, and specifically constructed barcode location markers for localization overwatch indoors.

The second element of the DARPA Urban Challenge derived driverless vehicle control system is the obstacle detection, which determines how far a vehicle can move in its intended driving direction without colliding with another vehicle, pedestrian or obstruction. Obstructions of any kind are objects that may be stationary or moving that intercept the vehicle's planned driving path at a future time and are large enough or deep enough to cause damage to either the vehicle or the obstacle. The size restrictions set for size are determined so as to not hurt or destroy a obstacle over a certain size (i.e., usually set not to hurt people) and not hurt the vehicle (set by vehicle and wheel size as compared to the obstacle). Some systems apply special algorithms to further characterize objects as pedestrians or specific vehicles, but this at the current state-of-the-art is less reliable than classification by location, movement, and size.

The third element of the DARPA Urban Challenge derived driverless vehicle control system includes driving controls providing automated control over the key driving functions including steering, acceleration, braking, engine controls, and other signaling (like brake lights and turn signals). In modern vehicles this is often easily done by accessing vehicle internal control buses (for instance, CAN). On older and specialized vehicles (for instance for many loader and unloader type vehicles) it may be necessary to refit with motors, linkages, and controllers to allow automatic control actuation. These controls will be usually controlled through an interconnection bus like CAN, but can also be directly controlled through other means including digital to analog and analog to digital interfaces.

The final element of the DARPA Urban Challenge derived driverless vehicle control system connects the roadgraph information base (road segments, lanes, waypoints, checkpoints, intersections, traffic flow rules like stops and speed limits, etc.) to commanded driving missions to be executed by the automated vehicle. Simple drive missions are typically described as a set of destination or checkpoints that the automated vehicle has to visit in the prescribed order (See, for example, FIG. 4). From any starting point, the vehicle invokes a planner that searches through the roadgraph for paths (which minimize some criteria like time, distance, paths preferred over particular road types, etc.) that get to the next checkpoint in list order. These paths are selected segments from the roadgraph assembled in the order to be driven. The planner then passes these plans to a driver module that executes them.

The driver module passes waypoints along the path to the next point driver. The next point driver makes smooth driving paths to the next point while observing potential obstacles. The precise smooth path to the next point is modified to the left or right so as to avoid potential collisions and if these collisions cannot be avoided, slows and eventually stops the vehicle prior to collision. Alternatively using a user interface that alerts the human driver can call for driving control to be passed from the automated system to the human driver (this only works for vehicles that have a human driver on-board—for a fully automated driverless system the equivalent is to slow and stop and pass control to a remotely located human or machine overseer who will take a corrective action including sending out a repair and maintenance crew, taking control of the remote vehicle by teleoperation, or safeing the stopped vehicle is some other way).

As described before, roadgraphs can include zones. Zones are defined as areas with specified entry and exit locations that connect to other parts of the roadgraph. In an automatic loader machine like an earthmover, a material handling forktruck, a container handler, etc., a zone will typically include a change of driving rules that allow free smooth movement between any points in the zone as long collision are avoided and designated locations and tasks to be accomplished are performed at these locations. Therefore a loading machine mission intersperses checkpoints with operations of procedures to be performed by payload handling systems at these checkpoints. Furthermore the smooth path planners, the driving module, and the next point drivers may be different from highway roadgraph systems as well. Essentially, the zone encapsulates a new alternative set of path definitions, RNDFs, driving rules, missions, etc., a new driving environment which is entered from the old and when complete returns back to the old environment.

FIG. 5 shows the plan used by one loader to enter the yard (Zone 1 through Checkpoint 1), find a load on the input side (Checkpoint 6), acquire a pallet, and move this pallet to the output side (Checkpoint 4). Then a second loader enters the yard from a different zone entry point (Checkpoint 3), finds and acquires a pallet from the output side (at Checkpoint 5), and takes the load to the left hand end of mission point (Checkpoint 7). Recoding these missions under assumption that each zone encodes an alternative driving environment they would look like:

Mission (First Truck): Mission (Second Truck):  Checkpoint 1: Zone 1 mission 1  Checkpoint 3: Zone 1 Mission 2  Checkpoint 8  Checkpoint 7 End End Zone 1 Mission 1: Zone 1 Mission 2  Checkpoint 1; Start  Checkpoint 3; Start  Checkpoint 6: Pallet Engagement  Checkpoint 5: Pallet Engagement  Checkpoint 4: Pallet Disengagement  Checkpoint 3; End  Checkpoint 1; End End

FIG. 6 shows how our expanded definition of zone supporting driving environments hierarchies might be used to separate different driving environment, each defined by an embedded RNDF map the road systems embedded within each zone. As an example, FIG. 6 shows how each warehouse of a multi-warehouse facility might be a submap of a larger map that encodes the streets interconnecting the warehouses. As another example, driving in the UK and France could be encoded as a world map that embedded the maps of France and the UK as zones, each embedding country specific maps [FIG. 2]. U.S. Pat. No. 8,509,982 describes how the zone idea might be incorporated into marking of hazardous areas where a human driver should assume vehicle control. [DARPA 2007 Rules] shows how zone can be incorporated to mark areas where free driving rules should supersede or take over from normal highway roadgraph deriving rules.

This description of a hierarchy of maps and objects and properties of objects within them has been diagrammed in the figures as hierarchy readily encoded in software by tree structures or graph structures. However it is equivalent to other software encodings including decision trees, data tables and databases which associate properties to objects, lists, arrays, or any other encoding means that support cyclic graphs and traversal of same, support hierarchical abstracts or trees, and means to associate descriptive properties to objects or nodes in these graphs or trees.

FIG. 7 is a schematic block diagram of a vehicle to which this invention is applicable, in this case a forklift of the kind used for warehousing operations. The vehicle includes a chassis, frame or platform 702 including an engine/powertrain 706 driving wheels 704. On-board computer 710 interfaces to memory 712 storing roadgraphs, mission instructions and other information. Computer 710 controls engine/powertrain 706 and steering 708 using information gathered from localization system 714 and collision avoidance system 716.

CITED REFERENCES

-   DARPA 2007 Rules,     http.//www.grandchallenge.org/grandchallenge/rules.html, and     http://archive.darpa.mil/grandchallenge/docs/Urban_Challenge_Rules_102707.pdf -   DARPA 2007 Route Network File (RNDF) and Mission Data File (MDF)     Formats,     http://www.grandchallenge.org/grandchallenge/docs/RNDF_MDF_Formats_031407.pdf,     and     http://archive.darpa.mil/grandchallenge/docs/RNDF_MDF_Formats_031407.pdf -   U.S. Pat. No. 8,509,982, Zone Driving, M. Montemerlo, D. Dolgov, C.     Urmson Keyhole Markup Language, KML Documentation,     https://en.wikipedia.org/wiki/Keyhole_Markup_Language -   TIGER Products, Topologically Integrated Geographic Encoding and     Referencing, https.//wvww.census.gov/geo/naps-data/data/tiger.html 

1. An autonomous transport vehicle, comprising: a frame, platform or chassis with a powertrain driving a set of wheels, and further including steering and braking systems; a memory on the vehicle storing road maps including roads and intersections; wherein the stored road maps further define a plurality of different zones, each zone respectively governed by a different set of driving rules; wherein the zones include material loading and unloading zones; a computer on the vehicle operative to implement a localization process to determine the location of the vehicle on the road maps, and an obstacle detection process to avoid structures external to the vehicle; wherein the computer is further operative to execute driving missions using the road maps, and wherein the missions include travel on the roads to pick up and drop off loads in the loading and unloading zones; and wherein one set of driving and obstacle detection rules are used during travel on the roads, and a different set of driving and obstacle detection rules are used within the loading and unloading zones.
 2. The autonomous transport vehicle of claim 1, wherein the obstacle detection rules within the loading and unloading zones are more relaxed as compared to the obstacle detection rules applicable to travel on roads between the loading and unloading zones.
 3. The autonomous transport vehicle of claim 1, wherein: the loading and unloading zones are within a warehouse; and the driving rules within the warehouse include free drive paths.
 4. The autonomous transport vehicle of claim 1, wherein the localization system includes GPS for latitude and longitude localization and visual sensing of environmental structures external to the vehicle.
 5. The autonomous transport vehicle of claim 1, wherein the localization system includes computer-readable location markers for localization indoors.
 6. The autonomous transport vehicle of claim 1, wherein the vehicle is a forklift.
 7. A method of maneuvering an autonomous material transport vehicle, comprising the steps of: receiving and storing a mission on the vehicle including a plurality of on-street and off-street zones defined by a road map; wherein the off-street zones include material loading and unloading zones; and wherein the on-street zones and the material loading and unloading zones impose different sets of driving and obstacle detection rules on the autonomous material transport vehicle while maneuvering with each respective zone.
 8. The method of claim 7, including paths between material loading and unloading zones having at least one waypoint associated with a destination.
 9. The method of claim 8, including the step of performing a particular operation at each waypoint.
 10. The method of claim 9, wherein the operation includes the execution of material-specific sensor or manipulation operations.
 11. The method of claim 7, wherein the vehicle is a forklift. 